wolffd@0: function CPDpot = convert_dbn_CPDs_to_tables_slow(bnet, evidence)
wolffd@0: % CONVERT_DBN_CPDS_TO_TABLES_SLOW Convert CPDs of (possibly instantiated) DBN nodes to tables
wolffd@0: % CPDpot = convert_dbn_CPDs_to_tables_slow(bnet, evidence)
wolffd@0: %
wolffd@0: % CPDpot{n,t} is a table containing P(n,t|pa(n,t), ev)
wolffd@0: % All hidden nodes are assumed to be discrete
wolffd@0: %
wolffd@0: % Non-vectorized method; this is less efficient for long sequences of observed Gaussian
wolffd@0: % nodes, because of the (unnecessary) repeated matrix inversion.
wolffd@0: 
wolffd@0: obs_bitv = ~isemptycell(evidence(:));
wolffd@0: [ss T] = size(evidence);
wolffd@0: ns = bnet.node_sizes(:);
wolffd@0: 
wolffd@0: CPDpot = cell(ss,T); 
wolffd@0: 
wolffd@0: t = 1;
wolffd@0: for n=1:ss
wolffd@0:   %ps = engine.bnet_parents{n};
wolffd@0:   ps = parents(bnet.dag, n);
wolffd@0:   e = bnet.equiv_class(n, 1);
wolffd@0:   if ~any(obs_bitv(ps))
wolffd@0:     CPDpot{n,t} = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, evidence{n,t});
wolffd@0:   else
wolffd@0:     CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps n], evidence(:,1));
wolffd@0:   end
wolffd@0: end
wolffd@0: for t=2:T
wolffd@0:   for n=1:ss
wolffd@0:     self = n+ss;
wolffd@0:     ps = parents(bnet.dag, self);
wolffd@0:     e = bnet.equiv_class(n, 2);
wolffd@0:     if ~any(obs_bitv(ps))
wolffd@0:       CPDpot{n,t} = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, evidence{n,t});
wolffd@0:     else
wolffd@0:       CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t));
wolffd@0:     end
wolffd@0:   end
wolffd@0: end       
wolffd@0: 
wolffd@0: